Apparatus and method for gathering analytics

ABSTRACT

A method and apparatus are provided for gathering analytics. In an embodiment, a method includes monitoring interactions of multiple users related to at least one of: multiple base content and multiple supplemental content. The supplemental content is associated with one or more products or services identified in the base content. The method also includes obtaining interaction information associated with the interactions. The method also includes selecting specified supplemental content to present to a specified user based on (i) the interaction information and (ii) specified base content presented to the specified user. The method also includes sending the specified base content and the specified supplemental content to an endpoint associated with the specified user.

CROSS-REFERENCE TO RELATED APPLICATIONS AND PRIORITY CLAIM

This application claims priority under 35 U.S.C. § 119(e) to U.S.Provisional Patent Application No. 61/945,646 filed on Feb. 27, 2014 andU.S. Provisional Patent Application No. 61/953,607 filed on Mar. 14,2014. The contents of the above-identified provisional patentapplications are hereby incorporated by reference in their entirety.

TECHNICAL FIELD

This disclosure is generally directed to software and more specificallyto a system and method for gathering analytics.

BACKGROUND

It is well-known that videos may be broadcast or provided through anumber of media, such as television, the Internet, DVDs, and the like.To finance such video broadcasts, commercial advertisements are oftenplaced in the videos. Commercials, however, require that a video bemomentarily interrupted while the commercials are displayed. Not only isthis annoying to viewers, but digital video recorders (DVRs) allow videoprograms to be pre-recorded. When the video programs are viewed, DVRsallow the viewers to fast-forward through commercials, thereby defeatingthe effectiveness and value of the commercials. When commercials arede-valued, costs are not adequately covered, and broadcast servicequality suffers as a result. In many cases, costs are made up bycharging viewers for video services.

In many conventional systems, a variety of different content has littleor no interactivity. This includes both videos and images. For example,when viewing video, different objects in the video are often merely partof a single video stream that is inseparable with respect to thedifferent objects. Static advertisements near the video stream relatedto the video are not very compelling as they are separated from thevideo in such a way that a user is not encouraged to interact with thestatic advertisement.

SUMMARY

This disclosure provides an apparatus and method for gatheringanalytics.

In a first embodiment, a method includes monitoring interactions ofmultiple users related to at least one of: multiple base content andmultiple supplemental content. The supplemental content is associatedwith one or more products or services identified in the base content.The method also includes obtaining interaction information associatedwith the interactions. The method also includes selecting specifiedsupplemental content to present to a specified user based on (i) theinteraction information and (ii) specified base content presented to thespecified user. The method also includes sending the specified basecontent and the specified supplemental content to an endpoint associatedwith the specified user.

In a second embodiment, a method includes monitoring one or more socialnetworks for one or more trending topics. The method also includesselecting supplemental content to be presented in association with basecontent. The supplemental content is selected based on (i) the one ormore trending topics and (ii) the base content, the supplemental contentassociated with one or more products or services identified in the basecontent. The method also includes sending the selected supplementalcontent to an endpoint associated with a user.

In a third embodiment, a method includes monitoring for multiplepredefined events. The method also includes, responsive to a usertriggering one of the predefined events, determining whether the user isassociated with a hot wallet. The method also includes, responsive tothe user being associated with the hot wallet, mining crypto-currency inthe hot wallet for currency information related to interactions of theuser.

Other technical features may be readily apparent to one skilled in theart from the following figures, descriptions, and claims.

BRIEF DESCRIPTION OF THE DRAWINGS

For a more complete understanding of this disclosure and its advantages,reference is now made to the following description, taken in conjunctionwith the accompanying drawings, in which:

FIG. 1 illustrates an example communication system that can be utilizedto facilitate communication between endpoints through a communicationnetwork according to this disclosure;

FIGS. 2A through 2E illustrate an example dynamic binding ofsupplemental content to base content according to this disclosure;

FIG. 3 illustrates an example platform for gathering analytical dataaccording to this disclosure;

FIG. 4 illustrates an example dynamic feedback system illustrating howbase content and supplemental content are optimized for a user accordingto this disclosure;

FIG. 5 illustrates an example process for dynamic feedback according tothis disclosure;

FIGS. 6A and 6B illustrate an example dynamic creation of base contentaccording to this disclosure;

FIGS. 7A and 7B illustrate example advanced collection of analyticsaccording to this disclosure;

FIG. 8 illustrates an example platform for a social network monitoringsystem according to this disclosure;

FIG. 9 illustrates an example process for configuring content based onsocial trends according to this disclosure;

FIG. 10 illustrates an example process for mining transactions withvideo according to this disclosure;

FIG. 11 illustrates an example process for mining transactions withwebsite analytics according to this disclosure; and

FIG. 12 illustrates an example computing device supporting variousfunctions according to this disclosure.

DETAILED DESCRIPTION

FIGS. 1 through 12, discussed below, and the various embodiments used todescribe the principles of this disclosure in this patent document areby way of illustration only and should not be construed in any way tolimit the scope of the disclosure. Those skilled in the art willunderstand that the principles of this disclosure may be implemented inany suitably arranged system.

FIG. 1 illustrates an example communication system 100 that can beutilized to facilitate communication between endpoints through acommunication network according to this disclosure. As shown in FIG. 1,the system 100 includes various endpoints 110, 120, and 130. In thisdocument, the term “endpoint” generally refers to any device, system, orother structure that communicates with another endpoint. Exampleendpoints 110, 120, and 130 include but are not limited to servers (suchas application servers and enterprise servers), desktop computers,laptop computers, netbook computers, tablet computers (such as APPLEIPADs), switches, mobile phones (such as IPHONE and ANDROID-basedphones), networked glasses (such as GOOGLE GLASS), networkedtelevisions, networked disc players, components in a cloud-computingnetwork, or any other device or component suitable for communicatinginformation to and from a communication network. Endpoints 110, 120, and130 may support Internet Protocol (IP) or any other suitablecommunication protocol(s). Endpoints 110, 120, and 130 may additionallyinclude medium access control (MAC) and physical layer (PHY) interfaces,such as those that conform to the IEEE 701.11 standard. An endpoint 110,120, and 130 can have a device identifier, such as a MAC address, andmay have a device profile that describes the endpoint.

A communication network 140 facilitates communications between theendpoints 110, 120, and 130. Various links 115, 125, and 135 couple theendpoints 110, 120, and 130 to the communication network 140. Thecommunication network 140 and associated links 115, 125, and 135 mayinclude but are not limited to a public or private data network, atelephony network, a local area network (LAN), a metropolitan areanetwork (MAN), a wide area network (WAN), a wireline or wireless network(such as GSM, CDMA, LTE, WIMAX, 5G, or the like), alocal/regional/global communication network, portions of acloud-computing network, a communication bus for components in a system,an optical network, a satellite network, an enterprise intranet, or anyother communication links or combinations of the preceding. Inparticular embodiments, portions of the links 115, 125, 135 or thecommunication network 140 may be on or form a part of the Internet.

Although the endpoints 110, 120, and 130 generally appear as being in asingle location in FIG. 1, various endpoints may be geographicallydispersed, such as in cloud computing scenarios. Also, each endpointcould represent a fixed or mobile device. When the endpoints 110, 120,and 130 communicate with one another, any of a variety of securityschemes may be utilized. As an example, in particular embodiments, theendpoints 110 and 120 may represent clients, and the endpoint(s) 130 mayrepresent one or more servers in a client-server architecture. Theserver(s) may host a website, and the website may have a registrationprocess whereby a user establishes a username and password toauthenticate or log into the website. The website may additionallyutilize a web application for any particular application or feature thatmay need to be served up to the website for use by the user.Additionally, in particular configurations, the communication betweenthe endpoints 110 and 120 may be facilitated using a communication paththrough the endpoint 130.

Various embodiments described in this patent document may benefit fromand/or utilize SMART CONTAINER technology from UNSAY, INC., which isbriefly described below and is described more fully in U.S. Pat. No.8,769,053 (which is hereby incorporated by reference in its entirety).This technology provides an innovative way for merchants to reach theircustomers online. In the traditional online sales model, merchants needto create search or display ads that show up when online consumers visitsearch engine sites or various web properties. If a consumer sees aninteresting ad related to a product or service, the consumer needs toleave his or her current activity and visit some other web destinationto discover more information or make an online purchase. Consumers havespecific online behavior patterns. If consumers are actively shopping,the traditional multistep model is workable. The traditional advertisingsales model requires that a consumer stop what he or she is doing andvisit some other online destination. However, if consumers are on socialsites interacting with friends, reading the news, playing games, orengaging in other online activities, they are much less likely to leavetheir current activities to visit some external Internet destinations.

The SMART CONTAINER model brings product information or a store to theconsumer. The SMART CONTAINER code/technology virally syndicates acrossthe web, for example, using components described with reference to FIGS.1 and 12 or using other components. It is ideal for those types ofdestinations that online consumers tend to frequent, such as socialnetworks and blogs. Regardless, if the SMART CONTAINER code is locatedon a web page, a blog article, a social network page or wall, or amobile device, a consumer can complete a transaction right there with noneed to be diverted to some external destination.

SMART CONTAINER objects are intelligent Internet objects that virallysyndicate and propagate across the web and other connected networks andmobile devices. They can be configured in a variety of ways to addressthe entire value chain of online marketing and shopping. This includesimpressions, clicks, lead generation, and performing e-commercetransactions. A modern shopping experience works best when interactivemedia is used. One of the most appealing forms of media for sales andshopping is video. It allows a much more lifelike representation thantext or static pictures. It also creates a much richer product browsingor shopping experience.

SMART CONTAINER code is normally configured with a video player window,a selection of products or services being offered, and a variety ofrelated video clips. This collection of video clips allows a consumer tolearn more about the products or services being offered. The consumercan select any of these offered items to get more details, all enclosedwithin the SMART CONTAINER technology.

The offered items (products or services) may be items being advertisedor sold. Depending on the type, the SMART CONTAINER code may allow aconsumer to request to be contacted, or even purchase the object, rightthere. The consumer need not leave his or her current activity or webpage. Offered items could also include or be associated with discountsor coupons. They may even be an opportunity to donate to a charity orpolitical campaign. Of course, sometimes it does make sense to visitanother Internet designation, and if appropriate the consumer cancertainly be linked there as well.

Because the SMART CONTAINER code handles all the complexity, it can turnthe simplest website into an instant e-commerce store. This enablesanyone to transact online without having to deal with the complexity ofsetting up an e-commerce site. For merchants with an e-commerce site, itreadily enables a much richer shopping experience. For the creativehobbyist or local band, it lets them readily sell directly to interestedconsumers. To support and promote them, supplemental items in the SMARTCONTAINER code called ON-DEMAND merchandise can be offered. Merchantscan custom design a selection of apparel with their art and graphics tobe sold along with their own creations. ON-DEMAND fulfillmentdynamically produces and ships their custom apparel for them,eliminating the need to manage inventory and providing their onlinecustomers with a richer line of products. Of course, because theirinstant e-commerce stores are based on SMART CONTAINER objects, it canalso propagate out onto all forms of viral syndication methods as well.

The SMART CONTAINER code is also auto-customizing according toparticular configurations. If a device is a traditional personalcomputer (PC) or laptop, it will render using optimal technology, whichfor this purpose could represent FLASH. On mobile devices such asIPHONEs, IPADs, or ANDROID phones, this means HTML5 or a nativeinteractive app will likely get used. The items in the SMART CONTAINERcode also know about each other according to particular configurations.When a video is playing, a container can update product and serviceobjects being shown that correspond with the particular sequence in avideo segment. It allows a “mini QVC” shopping channel to be created andsyndicated across the Internet. Beyond device type, there are otherdimensions of customization. Smaller devices and some environments suchas social sites restrict window sizes, so the SMART CONTAINER codeadapts. In addition, it may be appropriate to provide different contentbased on geolocation, so the SMART CONTAINER code can customize forthese, as well.

The SMART CONTAINER code virally syndicates across the Internetfollowing the more popular network paths. SMART CONTAINER objects can behosted on traditional web pages or blogs, contained in emails, operateon mobile devices, or propagate social networks. Because the SMARTCONTAINER code is flexible, it can also be set up in the form factor ofa display ad unit and distributed via ad servers on display advertisingnetworks. When the code exists on social networks like FACEBOOK, it canride the wave of user “likes.” For example, if a woman shopper likessome great shoes shown in a SMART CONTAINER object interface, the SMARTCONTAINER object can propagate directly to their “wall.” Now all of herfriends see the SMART CONTAINER object and can view or transact rightthere on their own walls. Of course, if any of her friends also “like”it, the SMART CONTAINER object propagates and rides the wave further outinto that branch of the social network, yielding a potential exponentialgrowth factor. The container does not necessarily involve products likeshoes. As another example, a container can support a politician runningfor office. His or her supporters may be passionate about a message and“like” it, again making it available to their networks. Now,similarly-minded political supporters can view those messages and, if somoved, donate to the cause. Yet another example is sports. In this case,a sports fan may wish to watch content on his or her high-definition(HD) large screen television. More and more users have interconnecteddevices such as ROKU and CHROMECAST devices, and the SMART CONTAINERcode may be sent to such IP television boxes, as well.

When merchants launch and syndicate their SMART CONTAINER objects ontothe Internet, they want to know how their campaigns are performing.SMART CONTAINER objects report back status on events and transactions ofinterest such as impressions, video views, clicks, leads, and sales. Allsuch events/transactions can be sent back as events occur, providingdetails on how they are doing. Because the containers are smart, theycan be instructed to change behavior, offer different clips, updateproducts, or to end when it is time to stop a marketing or salescampaign.

Another form of tracking relates to how the SMART CONTAINER code ispropagated. A merchant may wish to use affiliates to help syndicate themand pay them a percentage based on the transactions resulting from theirwork. SMART CONTAINER objects can be tagged with affiliate trackingidentifiers, allowing status reports and transactions from containerinstances or their descendants to be properly filtered. Another trackingusage may be for a politician to assign affiliate codes to his or hersupporters and be able to measure whose efforts result in the most newsupporters.

SMART CONTAINER objects are designed to be highly scalable according toparticular configurations. Rather than burden a single website withmassive traffic (which would result from a traditional model of bringingall consumers to a store), SMART CONTAINER code operates in adistributed manner. For example, the SMART CONTAINER code can executewhere it is located, such as on a blog, a social network, or a mobiledevice. SMART CONTAINER objects fetch their instructions when startedand then gather their product items and video streams from a worldwidedistributed content delivery network. This results in a highly scalablearchitecture, allowing millions of concurrent consumers.

By bringing the store to the customer, the SMART CONTAINER code enablesmany new ways for merchants to connect with their consumers withoutdisrupting the consumers' web activities. The end result is to connectthe consumers directly with the merchants, eliminating the middleman andpromoting a much more natural shopping experience.

The functionality of the above description may avail from any suitablecomponents, such as those described in FIGS. 1 and 12 or other suitablecomponents. The code itself may be written in any suitable format,including but not limited to JAVA, C++, C-sharp, HTML, HTML5, JAVASCRIPT, PYTHON, RUBY, and the like.

There exists a variety of content in the world that is independent,existing separate from any special containers such as that invoked bythe SMART CONTAINER code. Certain embodiments of this disclosure seek toharness the power of such content by dynamically binding supplementalcontent to underlying base content. As a simple example, a video may bestreamed from a content server such as provided by one of many videostreaming services. The base content represents any type of visual oraudio content, be it a picture, a streaming video, a live stream from aremote location, real-time content from the current location of adevice, a web page, or other types of visual content. The supplementalcontent represents additional information related to the base contentand/or a user accessing the base content. Supplemental content caninclude products or services, information about the products orservices, and the like.

FIGS. 2A through 2E illustrate an example dynamic binding ofsupplemental content to base content according to this disclosure. Asseen in FIGS. 2A through 2E, a base content 200 is generally shown. Thebase content 200 could represent any of the types of visual or audiocontent described above. The supplemental content represents additionalinformation related to the base content and/or a user accessing the basecontent. In some embodiments, the supplemental content can override themodule playing the base content and expand the functionality of themodule (such as with YOUTUBE).

In particular embodiments, the supplemental content may includeadditional information, configurable controls, selectableconfigurations, content transactional items such as products orservices, and the like. Content transactional items can be referred toas supplemental transactional items or supplemental transaction itemsand can be part of supplemental content. Although the displayable areafor the base content 200 is generally shown as having a rectangularboundary area, the displayable area for the base content 200 may take onother shapes. Additionally, the base content 200 may be shown in (orthrough) a virtually limitless number of devices, from mobile phones tocomputers to televisions.

As examples of the above, the base content 200 may be a video streamedthrough a video-based provider, such as YOUTUBE, VIMEO, NETFLIX, REDBOXINSTANT or others, being viewed on a computer, a mobile device, atelevision screen, or any other suitable device or devices. The basecontent 200 may also be a real-time view of content at a currentlocation being viewed through an electronic device such as GOOGLE GLASSor a real-time view in a mobile computing device such as a tablet orphone. In yet other configurations, the base content 200 may be animage. In still other configurations, the base content 200 may be a webpage.

Also shown in FIGS. 2A through 2E are non-limiting examples ofsupplemental content 210 a-210 e that are configured to dynamically bindto the base content 200. Although certain examples are provided, itshould be understood that such examples are non-limiting and otherconfigurations may be utilized as will become apparent to one ofordinary skill in the art having read this disclosure. In someconfigurations, the supplemental content may overlay the base content,whether partially transparent or not. Examples of supplemental content210 b and 210 e overlaying the base content 200 are shown in FIG. 2B(left position) and FIG. 2E. In other configurations, the supplementalcontent may be positioned outside of the base content 200, such as tothe left, right, top, bottom, or other positions. Examples ofsupplemental content 210 a, 210 c, and 210 d outside of a boundary areaof the base content 200 are shown in FIG. 2A, FIG. 2C (left position),and FIG. 2D.

In certain configurations, the supplemental content may be selectivelydisplayable and/or selectively “hideable,” such as due to user action orinaction. For example, in some configurations, a user interacting with acontainer for the base content may cause a menu with supplementalcontent to appear. Examples of these configurations are shown in FIGS.2B and 2C with the double-edged arrows representing selectivedisplay-ability or selective hide-ability.

In still other configurations, the supplemental content may beginoutside an area of the base content 200 and expand to cover, partiallytransparent or not, the base content 200. For example, as seen in FIG.2D, the position of the supplemental content 210 d on the left is justbelow a displayable area for the base content 200. However, in theposition of the supplemental content 210 d on the right (which may bethe result of interactivity by a user), the supplemental content 210 dexpands to at least partially overlay the base content 200 (as shown byan area 210 d′). A similar configuration is also shown in FIG. 2E exceptthat the supplemental content 210 e began as an overlay of the screenand an area 210 e′ covers an entire edge of the displayable area for thebase content 200.

In particular configurations, the supplemental content is independent ofthe base content 200 and is bound dynamically as the base content isdisplayed. For example, in particular settings, a web page may have acontainer (such as an embed code) that instantiates (loads or invokes)(i) the base content and (ii) the supplemental content. According tocertain configurations, a call for supplemental content can be based onwhat is being shown in the base content, with the supplemental contentspecifically relating to the base content. Additionally, thesupplemental content may be based on other parameters, such as a userprofile or a geolocation of the user viewing the base content. Asanother example, in other configurations, a page analyzer can review aweb page to determine locations where base content is contained andoverlay or adjust such base content.

According to this specification, the concept of “binding” refers toassociating supplemental content with base content, whereas “dynamicbinding” refers to associating content on the fly, such as upondetection of the base content. In particular configurations, the initialassociation may allow the subsequent sharing of both the supplementalcontent and the base content together, as will be described withreference to figures below. More particularly, in certainconfigurations, an initial dynamic binding yields a shareable container(which may or may not be instantiated by an embed code) that, upon beingshared to a new device, instantiates the underlying base content and thesupplemental content. In other configurations, no such container iscreated, and a dynamic binding or dynamic association of thesupplemental content is done for every playing of the video. In yetother configurations, supplemental content may be bound to a video, andthe particular content is dynamically determined when the video isrequested for playback.

A variety of technologies may be used for the above-described dynamicbinding. As an example non-limiting configuration, the supplementalcontent may be configured as one layer in a display, where the basecontent is another layer. In such configurations, the layer for thesupplemental content may be forward in the layers to allow an overlay asmight be appropriate. In other configurations, the supplemental contentmay simply be provided a positioning with respect to the base content.

In particular configurations, the supplemental content can bedynamically sized based on a determined size of the base content and/orthe spacing configurations for the device on which the base content andthe supplemental content will be displayed. In other configurations,given a particular size for the base content, the supplemental contentmay use the same size for a container that requests a slightlyreduced-size base content with extra room for the supplemental content.In implementing such a configuration, the technology can intercept arequest for the base content and redirect such a request in order torequest a container that, in turn, requests the base content and thenthe supplemental content. This latter configuration may be beneficialfor scenarios where the supplemental content does not overlay the basecontent.

In particular configurations, the supplemental content can be based onwhat is being shown in the base content 200. A variety of technologiesmay be utilized to recognize the base content 200. Additionally, inparticular configurations, a combination of technologies may beutilized. Further, as discussed in more detail below, the supplementalcontent can be customized depending on a user and/or device.

One problem faced by advertisers in an online environment is aninability to track advertising unit success from attraction totransaction, which (if captured) could yield analytical data about why auser clicked on an advertisement (among other things). To combat thisinability, advertisers have traditionally turned to third-party analyticproviders. However, such third-party analytic providers are only able torecord analytical data when a retailer or shopping site (which isusually separate from both the advertiser and the third-party analyticalprovider) places deep tags that link back to the third-party analyticalprovider. If the retailer or shopping site incorrectly places the tag,the data is lost. Given such a disparate multi-entity approach, certainembodiments of this disclosure provide a single platform that has theability to track certain psychographic, demographic, geographic, andbehavioral variables to give a holistic reflection of how, why, who,where, and when an individual, group, segment, organization, geographicregion or area, and/or profile has, will, or may interact with theplatform.

In particular configurations, such a platform may provide a way totarget the proper video or products to the proper audience based onprofiles and criteria, which in turn are based on data collected withinthe platform. As a non-limiting example, the gathered data may indicatethat a user shops at a certain hardware or home goods store and likes topurchase lumber and power tools (behavioral). Further, gathereddemographic information may indicate that this user is a male, within acertain age range or specific age, and has a certain level of income.Additionally, gathered geographic information may indicate that thisuser is someone who lives in a certain city. Yet additionally, thegathered data may be psychographic information that indicates that thisuser spends a lot of time viewing a particular news station and is amember of a certain political party (psychographic). Also, in particularembodiments, such a platform may obtain information from trackingcookies and information available within browsers, along with userprofile data/behavior in interacting with the platform. This gatheredinformation provides a unique ability to target a complete userexperience of targeting products and services to desired video.

FIG. 3 illustrates an example platform 300 for gathering analytical dataaccording to this disclosure. The platform 300 of FIG. 3 may beparticularly well-suited for transmitting and/or receiving dataassociated with the base content 200 and the supplemental content 210discussed with reference to FIGS. 2A-2E as well as the SMART CONTAINERobjects or code referenced above. As described here, the supplementalcontent 210 may contain interactive items that allow users to purchaseproducts or services being displayed within the base content 200.Additionally, as part of the supplemental content 210 or separatetherefrom, options to share the base content 200 and the supplementalcontent 210 can be provided. Non-limiting sharing examples includesharing via a social network like FACEBOOK, GOOGLE+, and LINKEDIN;sharing via an embed code (such as code inserted into a blog or awebsite); and sharing via an email.

The platform 300, according to certain configurations, can include datacollection components, such as a user interaction component 310 and auser attributes component 320. Additionally, as recognized by particularembodiments of the disclosure, some data may enter the platform asthird-party data 330 collected by systems other than the platform 300. Anon-limiting example of third-party data includes a profile, such as asocial media profile, that has been developed corresponding to a user(including, but not limited to, FACEBOOK shadow profiles).

The user attributes component 320 can generally record any number ofattributes, whether such attributes concern an actual user or devicesthe user utilizes. Non-limiting examples of user attributes include ageographical location, IP address, device identifier (such as a MACaddress), data in browser cookies, items posted in a header thatidentify a client (such as a GOOGLE CHROME browser), a time of day, andan environment in which the user is interacting (such as viewing thecontent on a FACEBOOK page).

The user interaction component 310 focuses on interaction informationthat includes user interactions, such as (1) user interactions with thebase content (like watching the content), (2) user interactions withsupplemental content (like information about past purchases), and (3)information about the user's sharing of the base content or thesupplemental content with other people. These types of interactions havebeen respectively labeled as base content viewing subcomponent 312,supplemental content subcomponent 314, and sharing subcomponent 316,respectively.

The base content viewing subcomponent 312 measures a user's engagementwith base content 200. For example, if the base content 200 is video,the base content viewing subcomponent 312 can measure some or all of thefollowing: how many seconds and what portions of the base content 200are watched, when a user pauses/fast-forwards or skips/rewinds, whatviews are watched (in configurations where multiple views of an eventare provided), eye tracking information related to where the user's eyesare focused on the video, and clicking information related to where auser moves a mouse, hovers, or clicks a mouse.

The supplemental content subcomponent 314 measures a user engaging withsupplemental content 210. Examples of measured items may include some orall of the following: when and how many times a user engages with aninteractive portion, what level of interaction occurs (such as inscenarios where multiple levels of interaction are provided), whether anitem is placed in a shopping cart (including the details of what isadded), and whether a product is purchased (including the details ofwhat is purchased).

The sharing subcomponent 316 measures what, to whom, and in what mannera user chooses to share content. For example, where a user chooses toembed content in a web page or a social blog, a unique number for theembed code when executed can inform the platform 300 as to where thecopied version of the shared base content or supplemental content isshared. As another example, where content is emailed or texted toanother user (which in particular configurations can be done through thesystem), data is gathered as to the particular other users, as well asany description provided by the sharing user. As yet another example,where content is shared to a social page (such as FACEBOOK, GOOGLE+,LINKEDIN, or the like), the particular user page can be noted.Additionally, when a “like” or “+1” occurs, the propagated content canfurther report back to the platform 300 where it arrives at next. Aswill be appreciated by one of ordinary skill in the art having read thisspecification, a chain of sharing can be derived to determine the mannerand at what speed content is propagated through various networks.

Additionally, according to particular embodiments, such gatheredstatistics can be used in an affiliate or multi-level-marketing (MLM)scenario to determine appropriate commissions, percentages of sales, orrevenue shares as a result of user actions. In MLM, independentnon-salaried participants, referred to as distributors (or associates,independent business owners, dealers, franchise owners, independentagents, etc.), are authorized to distribute products or services. Thedistributors are awarded retail profit from customers plus commission,not downlines, through a multi-level marketing compensation plan, whichis based upon the volume of products sold through sales efforts as wellas that of a downline organization. Independent distributors developorganizations by either building an active consumer network, who buydirect, or by recruiting a downline of independent distributors who alsobuild a consumer network base, thereby expanding the overallorganization. Additionally, distributors can also earn a profit byretailing products they purchased from the company at wholesale price.

Because containers having base content and supplemental content areself-contained, they can travel across heterogeneous networks (such asemail, FACEBOOK, GOOGLE+, and LINKEDIN), and the platform 300 can stilltrack the lineage (such as a downline in an MLM scenario) of the shares.Because this can be tracked, an affiliate or MLM determination can bemade. As an example of this, a particular transaction within a containercreated by code may occur. A search through the share history (back upthe line) can be performed to determine whether a portion of thetransaction should be credited to the user(s) in the “upline” thatshared the content.

Affiliate and MLM tracking can be done per user (regardless of contentshared) or per base content identifier (which may be several alternativeforms of the base content). Additionally, because of the level of datagathered, a commission may be modified using such gathered data. Forexample, a commission may be modified based on a volume of referrals, anactivity level of a sharing user, or a consideration of whether aparticular user created the base content (such as a video). Yet othercommission modifications based on gathered analytics may also beutilized.

Because all of these various types of data are gathered and/or processedby the same platform 300, a variety of correlations between differentdata avenues can yield answers as to what is effective and what is not.For example, the platform 300 can determine that a certain group ofusers are all interacting with certain interactive content and/orchoosing to share content around a same time during a display of basecontent. Such a correlation may indicate that a particular showing inthe base content triggers something in users to act.

Given the variety of different data collected by the platform 300, avariety of questions can be answered, such as but not limited to:

-   -   What do people of a certain graphic watch?    -   What interactive components best capture their attention?    -   When do certain groups of people shop?    -   Where do certain groups of people shop?    -   What devices do certain groups of people shop from?    -   What are certain users' purchasing habits?    -   What is the engagement rate and event tracking (such as start,        stop, mute, full screen)?    -   What is the digital video impression served (total ad displays)?    -   What is the unique Reach (the number of unique people who saw an        ad)?    -   What is the Frequency (the number of times a viewer is exposed        to the ad before clicking)?    -   What is the GRPs (the number of impressions in relation to the        size of the audience)?    -   What is working well?

According to particular embodiments of this disclosure, the platform 300may provide the ability to track impressions, a cart, a checkout page, apayment completion page, and transaction details (including refunds)within a single video e-commerce system. This also includes and extendsto the social aspect and advertising space where code propagating basecontent and supplemental content is outside or beyond the boundaries ofa single e-commerce platform or website.

For web designers, a webpage is allocated certain sections for design,form and function. Some areas are for content, advertising, socialinteractions, graphics, promotion, and the like. However, the platformaccording to particular embodiments here can exist in any area within awebsite (given size constraints) and allows for e-commerce transactionsno matter where it is displayed, without doing a click or redirect to acentral site to complete the transactions.

This e-commerce capability within a store provided by the code providesa unique ability to target content and e-commerce players based onbehavior and other factors mentioned above. Also, this capabilityprovides the platform with the ability to collect data in a singlerepository, including transactional details and refunds back to adecision engine.

In some embodiments, an e-commerce website can track details abouttransactions and user behaviors within its own website. As an example,the platform 300 can track transactions and purchase behaviors withinthe platform wherever the code is included, including other websites.The code can also behave as an ad unit to be included in any otherwebsite, allowing it to target the appropriate base content andsupplemental content based on the user and to track store andtransactional behavior accordingly. When using pre-recorded video, thesystem may use metadata from a repository; when using live video, thesystem may use information from a television guide or manual entry.

In particular embodiments, base content can be provided in differentangles. Each angle can include different metadata, and the supplementalcontent provided with each angle can vary. For example, some objects inthe base content may be seen from one angle but not another angle, andsupplemental content related to objects seen in only one angle may beprovided for that angle.

Also, in particular embodiments, the platform 300 may identifysupplemental content based on other merchants with similar products. Theplatform 300 may also identify supplemental content based on whatproducts sell well with a particular base content. The platform 300 mayalso identify supplemental content based on seasonality or what has soldwell to other users or friends similar to the user.

FIG. 4 illustrates an example dynamic feedback system 400 illustratinghow base content and supplemental content are optimized for a useraccording to this disclosure. The dynamic feedback system 400 includes adecision engine 410. In some embodiments, the decision engine 410 mayinclude or represent one or more servers (such as database servers andapplication servers) to apply decision logic as to what is going to workbest for a particular user. In an example embodiment, the decisionengine 410 could implement the platform 300 shown in FIG. 3.

In some embodiments, the decision engine 410 determines what basecontent 200 and supplemental content 210 should be presented to a user.The decision engine 410 can make this determination based on stored data402 concerning previously-gathered data or other data, user profile data415 concerning profiles of different users, and models or algorithms404, After the base content 200 and the supplemental content 210 aresupplied to a user, user interaction 420 with such base content 200 andsupplemental content 210 can occur. The user's actions can be recordedand fed back into the decision engine 410 and the profile data 415.

Using dynamic feedback system 400, the decision engine 410 in particularconfigurations can learn from data associated with previous interactionsand modify itself over time. According to particular configurations, themodels or algorithms 404 in the decision engine 410 can account forlonger-term data and shorter-term data. For example, the decision engine410 may determine that a certain supplemental content 210 and/or basecontent 200 is seasonal and distinguish such items from something thatis trending in a non-seasonal manner.

Although described as a decision engine 410, in differentconfigurations, the decision engine 410 can provide test data pointssimply for consideration of the response, tweaking a test database onreal-time feedback. The decision engine 410 can iterate through a numberof scenarios to yield an optimized solution.

Although the system 400 is described as optimizing both base content 200and supplemental content 210, in other configurations the dynamicfeedback system 400 may only optimize supplemental content 210, such asin scenarios where the base content 200 is fixed. In such scenarios, thedynamic feedback system 400 may only optimize the supplemental content210.

FIG. 5 illustrates an example process 500 for dynamic feedback accordingto this disclosure. For ease of explanation, the process 500 isdescribed with reference to the dynamic feedback system 400 of FIG. 4.At step 510, a user profile is obtained by a decision engine. The userprofile may include data gathered by the platform 300 of FIG. 3, otheruser data that has been gathered, data entered by the user, or acombination thereof.

At steps 520 and 530, the decision engine determines base content andsupplemental content that is optimal for the particular user. Asdescribed here, this decision may be based on the user profile, dataaccessible by the decision engine (such as user data, geographic data,demographic data, and the like), and algorithms or models. At step 540,a user interacts with one or both of the base content and thesupplemental content. Such user interactions may include any of avariety of different uses interactions that are apparent to one ofordinary skill in the art. At step 550, the actions of the user in suchinteractions are recorded. In particular configurations, this may happensimultaneously with step 540.

At step 560, feedback is provided to the decision engine. This feedbackmay update the aggregate data collected for a plurality of users and/orthe algorithm or model used for making decisions. At step 570, feedbackis provided to the user profile. This feedback, among other things, maybe used to update the user profile. The feedback may also be used toupdate other user profiles or used in presenting content to other users.For example, if one user lives in a state and cheers for a certainsports team related to that state, another user in that same state maycheer for the same sports team.

FIGS. 6A and 6B illustrate an example dynamic creation of base contentaccording to this disclosure. For the purpose of illustration, the basecontent in FIGS. 6A and 6B will be described as a video, although othertypes of base content may be created.

A particular video to be displayed to a user may be determined by thedecision engine 410. With a host of data available, the decision engine410 may determine what sequence of segments is appropriate and whatpre-roll and post-roll segments are also appropriate. In particularconfigurations, the different segments may correspond to differentcamera angles or different versions of the same movie or event.

With reference to FIG. 6A, segments A1, B1, and C1 have been selectedalong with a fifteen second pre-roll and a thirty second post-roll. Thefifteen second pre-roll segment may have been determined based on astatistical analysis that a fifteen second pre-roll works better than athirty second pre-roll because the user is female. Further, the A1, B1,and C1 segments may have been determined because the camera angle isbetter for products that a female is likely to purchase.

With reference to FIG. 6B, segments A2, B1, and C2 have been selectedalong with a thirty second pre-roll and a thirty second post-roll. Thethirty second pre-roll segment may have been determined based on astatistical analysis that a thirty second pre-roll works better than afifteen second pre-roll because the viewer is male and males tend toenjoy the longer pre-roll. Further, the A2 and C2 segments may be havebeen chosen because the camera angle is better for products that a maleis likely to purchase. Additionally, B1 (although more likely to haveproducts that a female would purchase) may have been determined to be ofinterest to a male viewer based on a previous purchase for a girlfriendor spouse. The above are non-limiting examples of how base content canbe dynamically created. Other examples are apparent to one of ordinaryskill in the art after having read this specification.

FIGS. 7A and 7B illustrate example advanced collection of analyticsaccording to this disclosure. With reference to FIG. 7A, base content700 and supplemental content 710 are displayed. A sensor 790 records auser's eye movement in viewing the base content 700 and supplementalcontent 710. Without the user interactively selecting something, thesensor 790 can gather data that indicates the user is interested insomething within the video. As an example, the sensor 790 can sense thewhites of the user's eyes to determine the angle at which the eyes arepositioned. Moreover, the sensor 790 can detect how little or much theeyes are opened to change a changing level of interest. Yet other eyetracking technologies will become apparent to one or ordinary skill inthe art. In particular configurations, the sensor 790 may detect thatthey eyes have moved over a virtual hotspot that is invoked uponfocusing on the virtual hot spot for a determined time. The focus timemay be a threshold period of time that is dynamically changed,predetermined, or manually adjusted. Such eye tracking technologies maybe implemented in mobile devices, computers, set-top boxes, or otherdevices. When such eye-tracking data is gathered, the data can be fedback to the decision engine 410 and the user's profile as describedabove.

With reference to FIG. 7B, a geographic location of an item 760 isknown. Upon knowledge of the view 780 (two-dimensional view orthree-dimensional spatial view) of a user 750, a level of interest ofthe object may be determined. As a non-liming example, usinggeo-location sensors, compass data, and inclinometer on GOOGLE GLASSES,the three-dimensional geo-spatial view of a wearer can be determined.Further, upon knowledge of location of the objects, when the two arecorrelated, a determination as to a level of interest in a particularobject can be determine based on how long the object is viewed or howlong the person is in front of the object. Such location focus data canbe fed back to the decision engine 410 and the user's profile asdescribed above.

Embodiments of this disclosure also recognize and take into account thata problem faced by vendors in an online environment is when to postadvertisements and when to determine when to sell different products.Sometimes, when the vendor has a certain number of posting ofadvertisements set over a period of time, the vendor must manually makeany adjustments to the number of postings for that period of time ormanually make adjustments to the period of time. During the time betweenrecognition of needing to make the adjustments and manually making theadjustments, the vendor is losing out on sales.

Certain embodiments of this disclosure provide for the monitoring ofsocial networks and news feeds for trending topics. For example, if asports team is playing in a sporting event and wins, there may be alarge number of social media updates regarding the winning team.Additionally, in some embodiments, a vendor may be able to have a higherfrequency of postings if the vendor's products relate to the event,sport, winning team, and the like. In other embodiments, vendors'products may relate to a trending topic and are displayed in higherpriority.

FIG. 8 illustrates an example platform 800 for a social networkmonitoring system according to this disclosure. The platform 800 of FIG.8 may be well-suited for serving up and/or receiving data associatedwith the base content 200 and the supplemental content 210 discussedwith reference to FIGS. 2A through 2E, as well as the SMART CONTAINERobjects or code referenced above. As described here, the supplementalcontent 210 may contain interactive items that allow users to purchaseproducts or services being displayed within the base content 200.Additionally, as part of the supplemental content 210 or separatetherefrom, options to share the base content 200 and the supplementalcontent 210 can be provided. Non-limiting sharing examples includesharing via a social network like FACEBOOK, GOOGLE+, and LINKEDIN;sharing via an embed code (such as when inserted into a blog or awebsite); and sharing via an email.

The platform 800 according to certain configurations can have a video805, a decision engine 810, and a database 815. The decision engine 810can be configured to monitor trending topics 835 from social networks820 and relate that with vendor information 825 in the database 815. Thedecision engine 810 can be one example of the decision engine 410 ofFIG. 4. In some embodiments, vendor information 825 may include but isnot limited to what products the vendor currently sells, what logos areassociated with the vendor, what markets are related to the vendor, andthe like. Additionally, as recognized by particular embodiments of thisdisclosure, the database 815 may also include customer information 830.Customer information 830 may indicate trends and statistics oncustomers' purchases. A non-limiting example of the customer information830 may be to indicate specific interests of specific customers.

In some embodiments, the decision engine 810 can be configured toretrieve trending topics 835 from social networks 820. The trendingtopics 835 can be keywords, hashtags, categories, and the like. As anexample, the decision engine 810 can match keywords and hashtags andrepost pre-loaded text automatically. The decision engine 810 canleverage trending words, phrases, and hash tags to maximize views andsearch engine optimization.

In other embodiments, the decision engine 810 can be configured toretrieve data 820 and vendor information 825. The decision engine 810can identify trending topics 835 and target customers that havetendencies for those trending topics by searching through customerinformation 830. As an example, if a trending topic is a sporting event,the decision engine 810 may be configured to search customer information830 for customers who purchase products related to the sport of thesporting event and present postings for vendors with similar types ofproducts. As a particular example, the decision engine 810 may selectspecific vendors based on the trending topics 835 and/or the customerinformation 830. Customer information 830 may also be part of a userprofile.

Based on the extracted information from the trending topics 835,information on available video assets (such as the video 805) and itemsin the database 815, the decision engine 810 can provide base contentand supplemental transactional items that complement the trend. In otherwords, where a social wave is occurring, the decision engine 810analyzes the wave and identifies what content is available to complimentthe energy of the wave. Dynamically-created containers can be injectedinto the wave, where the video 805 is the attracting mechanism and theproducts and services of the supplemental content may be bought onimpulse. To engage with the trend and drive traffic to web destinationscontaining SMART CONTAINERS in particular configurations, the decisionengine 810 may also make general or targeted supplemental social mediaposts, post blog entries on web sites, and send or augment promotionalemails.

FIG. 9 illustrates an example process 900 for configuring content basedon social trends according to this disclosure. For ease of explanation,the process 900 is described with reference to the platform 800 of FIG.8. At step 910, a decision engine identifies trending topics in socialnetworks and media. At step 920, the decision engine identifies customerinformation and vendor information from a database. The database mayinclude a historical record of past purchases, as well as informationrelated to vendors and customers. At step 930, the decision engine candetermine what containers should be created. The determination may bebased on a combination of trending topics, vendor information, andcustomer information. For example, the decision engine can identify thetrending topics and then select vendors that relate to those trendingtopics and customers that are more likely to purchase products orservices related to those trending topics. At step 940, the decisionengine may inject the container into the social wave.

In addition, embodiments of this disclosure provide a mechanism forutilizing crypto-currency (including vanity crypto-currency) as a methodof collecting analytics for a web page, smart object, or element(s)within a page or object. Crypto-currency can be accumulated in a varietyof ways. In a conventional method, crypto-currency can be earned orawarded based on user interaction events or programmatically triggeredevents. Embodiments of this disclosure can obtain and use any and allunique identifiable information that is available (such as IP address,user agent, and browser plugins/fonts/settings), which provides theability to track users and obtain user information without the userneeding to be logged into a system.

Examples of analytics and information that can be collected viacrypto-currency and block chain systems include views, impressions,uniqueness, time, events, transactions, campaign and/or targeted subpage or element, social, geo-location, audience data, content data, andthe like. A block chain is a public ledger that records crypto-currencytransactions. Maintenance of the block chain is performed by a networkof communicating nodes running crypto-currency software. Transactionsare broadcast to this network, and network nodes can validatetransactions, add them to their copy of the ledger, and broadcast theseledger additions to other nodes. The block chain is a distributeddatabase, and each network node stores its own copy of the block chainin order to independently verify the chain of ownership of any and everycrypto-currency.

Various embodiments of this disclosure provide for miningcrypto-currency for analytics through video and product interaction aswell as website analytics. In video and product interactions, definedactions (such as a video start, watching a pre-roll, or opening aproduct flow) could start client-side mining of a crypto-currency. Withsite analytics, background JAVASCRIPT or other mechanism on the clientside could trigger once a page has loaded to start mining acrypto-currency and could continue for its duration on that page. Anyinformation mined from crypto-currency can be referred to as currencyinformation. This process could also be used to track ad impressions andduration on screen for third party sites, such as by using an HTML5 adwith the mining JAVASCRIPT that is embedded. Any of the processes herecould be cross-referenced from the block chain to access logs in orderto track interaction duration, as well as actions that have triggeredmining to occur (such as playing a video or visiting a particular pageon a site).

Some embodiments of this disclosure also provide for usingcrypto-currency transactions for analytics. In video and productinteractions, defined actions and checkpoints (such as a video start,watching a pre-roll, waypoints on the video, or opening a product flow)could trigger a transaction from a “hot wallet” that is filled withpre-mined coins, which would allow memos to be added with details aboutthe interaction type. With site analytics, background JAVASCRIPT orother mechanism on the client side could trigger once a page has loadedto send an initialization transition and, at designated or randomcheckpoints, trigger additional transactions and could continue for itsduration on that page. This process could also be used to track adimpressions and duration on screen for third party sites, such as byusing an HTML5 ad with the mining JAVASCRIPT that is embedded. Any ofthe processes here could be aggregated from the block chain to build acomplete analytics picture for each user.

Some embodiments of this disclosure further provide for the use of thecrypto-currency block chain and wallet to calculate and accumulatestatistics within a web page, smart object, or element within a webpage. These embodiments of this disclosure can recognize and take intoaccount that the crypto-currency block chain provides a distributedpublic ledger of transactions. This verifies chain of ownership of everytransaction. Each transaction would represent the analytics beingtracked.

Moreover, some embodiments of this disclosure provide for theutilization of crypto-currency and the block chain to instantiate ananalytics collection system associated with events, triggered andautomatic, within a web page, smart object, or element(s) within a webpage or smart object, which can be referred to as a “target object”. Insome example embodiments, the system tracks start and stop times forduration of video watched utilizing the block chain. In other exampleembodiments, the system tracks purchase and transactional events withinsmart object e-commerce systems. In yet other embodiments, the systemtracks “target objects” that can include the following:

-   -   Views: collection of presentation of the target object    -   Impressions: collection of instantiation of the target object    -   Uniqueness information: collection of user and computer        information to track uniqueness of a viewer over time. For        example, reporting can be done to determine variable time        windows and uniqueness of visitors and visitor events.    -   Time information: collection of length of time accumulated for        time analytics. For example, with time-stamping within the block        chain, analytics can be tracked for start and stop time stamps        for a “target object.” In some examples, this could be time        spent on page, time video played, time video paused, average        place paused or stopped, and net time spent.    -   Events: General events with a “target object” to include        interactive events such as “click” or “hover” or “mouse” or load        or unload events. These events can be triggered automatically or        manually.    -   Transactions within an e-commerce system, shopping cart, or        smart object: These could include stock keeping unit (SKU),        cart, cart abandonment, affiliate or referrer attribution, and        other elements tracked. This type of tracking finds popular        products and conversion reports. This type of tracking also        finds effective and ineffective portions of the site based on        referral, completion, and abandonment.    -   Campaign and/or targeted sub-page or element: this type of        tracking allows additional layers of analytics within a page or        element    -   Social interactions: within a smart object and embedded elements        within social networks would be able to be tracked utilizing the        block chain without requiring social networks to embed        additional code    -   Embedded smart objects or elements on third party pages: could        be able to collect analytics from a smart object without        requiring third party pages to change or embed additional        elements or scripts beyond the “target object” being embedded.    -   Geo-location information: could be tracked and collected based        on mobile or internet protocol information of the user    -   Audience data: tracks data of the types of people and their        information to determine audience makeup and loyalty    -   Content data: tracks analytics that allows the content provider        that ability to setup comparative analysis between multiple        content elements to test and determine content effectiveness.

FIG. 10 illustrates an example process 1000 for mining transactionsvwith video according to this disclosure. For ease of explanation, theprocess 1000 is described with reference to the platforms 300, 800 ofFIGS. 3 and 8. At step 1002, responsive to a user loading a videoplayer, a decision engine may identify the user. At step 1004, thedecision engine may determine whether the user has an existing wallet.The wallet may be a wallet for a crypto-currency. If so, the engine mayget the wallet address at step 1006. Otherwise, the engine may create awallet address at step 1008. At step 1010, the decision engine may startmining or send the transaction with metadata to a block chain.

At step 1014, responsive to a user playing a video, the engine may startmining or send the transaction with metadata to a block chain. At step1016, responsive to a user stopping a video, the engine may start miningor send the transaction with metadata to a block chain. At step 1018,responsive to a user triggering a predefined event, the engine may startmining or send the transaction with metadata to a block chain.

At step 1020, a transaction or mining posts to a block chain. Theposting can come from operations 1010-1016, as well as othertransactions. At step 1022, the block chain creates a database entry.The entry can be of the transaction and information related to thetransaction. At step 1024, the engine can query the database to reportthe analytics. The report can be sent to a vendor or merchant.

FIG. 11 illustrates an example process 1100 for mining transactions withwebsite analytics according to this disclosure. For ease of explanation,the process 1000 is described with reference to the platforms 300, 800of FIGS. 3 and 8.

At step 1102, responsive to a user loading a website or smart object, adecision engine may identify the user. At step 1104, the decision enginemay determine whether the user has an existing wallet. The wallet may bea wallet for a crypto-currency. If so, the engine may get the walletaddress at step 1106. If not, the engine may create a wallet address atstep 1108. At step 1110, the engine may start mining or send thetransaction with metadata to a block chain.

At step 1112, a transaction or mining posts to a block chain. At step1114, the block chain creates a database entry. The entry can be of thetransaction and information related to the transaction. At step 1116,the engine can query the database to report the analytics. The reportcan be sent to a vendor or merchant.

As used in this disclosure, gathering of analytics may be described asbeing performed in response to certain actions or events, but thegathering of analytics could be performed in response to any otheractions or events (including events within smart objects and eventswithin a smart store).

FIG. 12 illustrates an example computing device 1200 supporting variousfunctions according to this disclosure. The computing device 1200 herecould be used to implement any of the techniques or functions describedabove, including any combination of the techniques or functionsdescribed above. The computing device 1200 may generally be adapted toexecute any of suitable operating system, including WINDOWS, MAC OS,UNIX, LINUX, OS2, IOS, ANDROID, or other operating systems.

As shown in FIG. 12, the computing device 1200 includes at least oneprocessing device 1212, a random access memory (RAM) 1214, a read onlymemory (ROM) 1216, a mouse 1218, a keyboard 1220, and input/outputdevices such as a disc drive 1222, a printer 1224, a display 1226, and acommunication link 1228. In other embodiments, the computing device 1200may include more, less, or other components. Computing devices come in awide variety of configurations, and FIG. 12 does not limit the scope ofthis disclosure to any particular computing device or type of computingdevice.

Program code may be stored in the RAM 1214, the ROM 1216 or the discdrive 1222 and may be executed by the at least one processing device1212 in order to carry out the functions described above. The at leastone processing device 1212 can be any type(s) of processing device(s),such as one or more processors, microprocessors, controllers,microcontrollers, multi-core processors, and the like. The communicationlink 1228 may be connected to a computer network or a variety of othercommunicative platforms, including any of the various types ofcommunication networks 140 described above. The disc drive 1222 mayinclude a variety of types of storage media such as, for example, floppydrives, hard drives, CD drives, DVD drives, magnetic tape drives, orother suitable storage media. One or multiple disc drive 1222 may beused in the computing device 1200.

Note that while FIG. 12 provides one example embodiment of a computerthat may be utilized with other embodiments of this disclosure, suchother embodiments may utilize any suitable general-purpose orspecific-purpose computing devices. Multiple computing devices havingany suitable arrangement could also be used. Commonly, multiplecomputing devices are networked through the Internet and/or in aclient-server network. However, this disclosure may use any suitablecombination and arrangement of computing devices, including those inseparate computer networks linked together by a private or publicnetwork.

The computing devices 1200 could represent fixed or mobile devices, andvarious components can be added or omitted based on the particularimplementation of a computing device. For example, mobile devices couldinclude features such as cameras, camcorders, GPS features, and antennasfor wireless communications. Particular examples of such mobile devicesinclude IPHONE, IPAD, and ANDROID-based devices.

Although the figures above have described various systems, devices, andmethods related to gathering analytics, various changes may be made tothe figures. For example, the designs of various devices and systemscould vary as needed or desired, such as when components of a device orsystem are combined, further subdivided, rearranged, or omitted andadditional components are added. As another example, while variousmethods are shown as a series of steps, various steps in each methodcould overlap, occur in parallel, occur in a different order, or occurany number of times. In addition, examples of graphical presentationsare for illustration only, and content can be presented in any othersuitable manner. It will be understood that well-known processes havenot been described in detail and have been omitted for brevity. Althoughspecific steps, structures, and materials may have been described, thisdisclosure may not be limited to these specifics, and others may besubstituted as it is well understood by those skilled in the art, andvarious steps may not necessarily be performed in the sequences shown.

In some embodiments, various functions described in this patent documentare implemented or supported by a computer program that is formed fromcomputer readable program code and that is embodied in a computerreadable medium. The phrase “computer readable program code” includesany type of computer code, including source code, object code, andexecutable code. The phrase “computer readable medium” includes any typeof medium capable of being accessed by a computer, such as read onlymemory (ROM), random access memory (RAM), a hard disk drive, a compactdisc (CD), a digital video disc (DVD), or any other type of memory. A“non-transitory” computer readable medium excludes wired, wireless,optical, or other communication links that transport transitoryelectrical or other signals. A non-transitory computer readable mediumincludes media where data can be permanently stored and media where datacan be stored and later overwritten, such as a rewritable optical discor an erasable memory device.

It may be advantageous to set forth definitions of certain words andphrases used throughout this patent document. The terms “application”and “program” refer to one or more computer programs, softwarecomponents, sets of instructions, procedures, functions, objects,classes, instances, related data, or a portion thereof adapted forimplementation in a suitable computer code (including source code,object code, or executable code). The terms “transmit,” “receive,” and“communicate,” as well as derivatives thereof, encompasses both directand indirect communication. The terms “include” and “comprise,” as wellas derivatives thereof, mean inclusion without limitation. The term “or”is inclusive, meaning and/or. The phrase “associated with,” as well asderivatives thereof, may mean to include, be included within,interconnect with, contain, be contained within, connect to or with,couple to or with, be communicable with, cooperate with, interleave,juxtapose, be proximate to, be bound to or with, have, have a propertyof, have a relationship to or with, or the like. The phrase “at leastone of,” when used with a list of items, means that differentcombinations of one or more of the listed items may be used, and onlyone item in the list may be needed. For example, “at least one of: A, B,and C” includes any of the following combinations: A, B, C, A and B, Aand C, B and C, and A and B and C.

While this disclosure has described certain embodiments and generallyassociated methods, alterations and permutations of these embodimentsand methods will be apparent to those skilled in the art. Other changes,substitutions, and alterations are also possible without departing fromthe invention as defined by the following claims.

What is claimed is:
 1. A processor-implemented method for gatheringanalytics, the method comprising: monitoring user interactions of usersof a plurality of user compute devices related to at least one of:multiple base content and multiple supplemental content, eachsupplemental content from the multiple supplemental content associatedwith one or more products or services identified in a base content anddisplayed within the base content when the base content is displayed ona user compute device from the plurality of user compute devices;obtaining and aggregating user interaction information associated withthe monitored user interactions of users of the plurality of usercompute devices; selecting specified supplemental content from themultiple supplemental content to provide to a specified user computedevice based on (i) the obtained and aggregated interaction informationand (ii) specified base content provided to the specified user computedevice; dynamically binding the specified base content and the selectedspecified supplemental content to form a shareable media container, theshareable media container, when instantiated, configured to render in aninterface, the specified base content and the selected specifiedsupplemental content, the interface including a sharing user interfaceand a transaction user interface, the sharing user interface configuredto allow a user to share an instance of the shareable media container toanother compute device such that upon being shared to another computedevice embed code instantiates the specified base video content and theselected specified supplemental content, the transaction user interfaceconfigured to allow the user to initiate and complete an e-commercetransaction associated with at least one of the one or more products orservices within the shareable media container without diverting from thespecified base content, the shareable media container being configuredto monitor and collect information related to the e-commercetransaction; and sending the shareable media container including thedynamically bound specified base content and the selected specifiedsupplemental content to an endpoint associated with the specified usercompute device to be viewed by a user of the specified user computedevice.
 2. The processor-implemented method of claim 1, furthercomprising: selecting the specified base content to present to the userof the specified user compute device based on the obtained andaggregated interaction information and user information associated withthe specified user compute device.
 3. The processor-implemented methodof claim 1, wherein: selecting the specified supplemental content topresent to the user of the specified user compute device is furtherbased on user information; and the user information includes at leastone of demographic information, psychographic information, andgeolocation information associated with the user of the specified usercompute device.
 4. The processor-implemented method of claim 1, wherein:selecting the specified supplemental content to present to the user ofthe specified user compute device is further based on user information;and the user information comprises a user profile, the user profilecomprising data related to interests of the user of the specified usercompute device.
 5. The processor-implemented method of claim 4, furthercomprising: updating the user profile with the obtained and aggregatedinteraction information; and sending additional supplemental content tothe endpoint based on the updated user profile.
 6. Theprocessor-implemented method of claim 1, wherein: the monitored userinteractions of the users of the plurality of user compute devicesincludes user-initiated sharing of base content from the multiple basecontent and/or supplemental content from the multiple supplementcontent; and the obtaining the user interaction information includesobtaining information identifying where the base content and/or thesupplemental content is shared, how the base content and/or thesupplemental content is shared, or with what users the shared basecontent and/or the shared supplemental content is shared.
 7. Theprocessor-implemented method of claim 1, wherein the obtaining userinteraction information comprises: retrieving a number of times that atleast one user of a user compute device of the plurality of user computedevices interacts with base content from the multiple base content orsupplemental content from the multiple supplement content, a level ofinteraction of the at least one user with base content or supplementalcontent, whether at least one of the one or more products or services isplaced into a shopping cart, or whether at least one of the one or moreproducts or services is purchased.
 8. The processor-implemented methodof claim 4, wherein: the monitored user interactions comprise eyetracking information; and the obtaining the user interaction informationcomprises obtaining information on instances of eyes of a user focusingfor a threshold period of time on hotspots in the base content orinstances of the eyes of the user focusing for the threshold period oftime on the supplemental content.
 9. The processor-implemented method ofclaim 1, wherein: the multiple base content comprises content viewed atmultiple different angles; and the selecting the specified supplementalcontent comprises selecting different supplemental content from themultiple supplement content for at least two of the multiple angles. 10.The processor-implemented method of claim 1, wherein: the monitored userinteractions comprise sharing base content or supplemental content bydistributors in a downline in a multi-level-marketing organization; andthe obtaining the user interaction information comprises retrievinginformation associated with transactions from shared content.
 11. Theprocessor-implemented method of claim 10, further comprising:identifying distributors in an upline for the transactions; and loggingthe transactions with each of the distributors in the upline.
 12. Aprocessor-implemented method, comprising: monitoring a plurality of usercompute devices for user interactions with a plurality of base videocontent and associated supplemental content, the user interactionsincluding user-initiated sharing of at least one of base video contentfrom the plurality of base video content and supplemental content, thesupplemental content associated with one or more products or servicesidentified in a base video content and displayed within the base videocontent when the base video content is displayed on a user computedevice from the plurality of user compute devices; aggregating userinteraction information associated with the monitored user interactionsof the plurality of user compute devices, the user interactioninformation included user-initiated sharing information; selectingsupplemental content to provide to a given user compute device based on(i) the aggregated user interaction information and (ii) a specifiedbase video content requested for presentation at the given user computedevice; dynamically binding the selected supplemental content and thespecified base video content to form a shareable media container, theshareable media container, when instantiated, configured to render in aninterface, the specified base content and the selected supplementalcontent, the interface including a sharing user interface and atransaction user interface, the sharing user interface configured toallow a user to share an instance of the shareable media container toanother compute device such that upon being shared to another computedevice embed code instantiates the specified base video content and theselected supplemental content, the transaction user interface configuredto allow the user to initiate and complete an e-commerce transactionassociated with at least one of the one or more products or serviceswithin the shareable media container without diverting from thespecified base content, the shareable media container being configuredto monitor and collect information related to the e-commercetransaction; and providing the shareable media container including thedynamically bound requested base video content and the selectedsupplemental content to an endpoint associated with the given usercompute device for viewing by a user of the user compute device.
 13. Theprocessor-implemented method of claim 12, wherein the user-initiatedsharing information includes identification of the base video content orthe supplemental content, how the base video content or the thesupplemental content is shared, and where the base video content or thethe supplemental content is shared.
 14. The processor-implemented methodof claim 12, wherein the user-initiated sharing information includesidentification of a recipient user compute device of shared base videocontent or shared supplemental content.
 15. The processor-implementedmethod of claim 12, wherein the monitoring the plurality of user computedevices for user interactions with the plurality of base video contentand associated supplemental content further comprises monitoring usereye tracking data, the user eye tracking data including information oninstances of eyes of a user focusing for a threshold period of time onone or more hotspots in the base video content or instances of the eyesof the user focusing for the threshold period of time on thesupplemental content.
 16. The processor-implemented method of claim 12,wherein: the plurality of base video content comprises video contentviewed at multiple different angles; and the selecting supplementalcontent to provide to the given user compute device further comprisesselecting supplemental content for at least two of the multiple angles.17. The processor-implemented method of claim 12, further comprisingmonitoring one or more social networks for one or more trending topics;and wherein the selecting supplemental content to provide to the givenuser compute device is further based on the one or more trending topics.18. The processor-implemented method of claim 12, further comprising:monitoring activity of the given user compute device for one or morepredefined events; responsive to the given user compute devicetriggering one of the predefined events, determining whether the givenuser compute device is associated with a hot wallet; and responsive tothe given user compute device being associated with the hot wallet,mining crypto-currency in the hot wallet for currency informationrelated to user interactions of the user compute device.
 19. Theprocessor-implemented method of claim 18, wherein the one or morepredefined events include at least one of: product interactions, loadinga video player, playing a video, stopping the video, watching apre-roll, and/or opening a product flow.
 20. The processor-implementedmethod of claim 18, wherein the currency information includes at leastone of: a number of views, a number of impressions, uniquenessinformation, time information, events, transactions, socialinteractions, geo-location information, audience data, and content data.21. A processor-implemented method, the method comprising: monitoring aplurality of compute devices for user interactions with a plurality ofbase video content and associated supplemental content, the userinteractions including user-initiated sharing of at least one of basevideo content from the plurality of base video content and supplementalcontent, the supplemental content associated with one or more productsor services identified in a base video content and displayed within thebase video content when the base video content is displayed on a computedevice from the plurality of compute devices; aggregating userinteraction information associated with the monitored user interactionsof the plurality of compute devices, the user interaction informationincluded user-initiated sharing information; selecting supplementalcontent to provide to a given compute device based on (i) the aggregateduser interaction information and (ii) a specified base video contentrequested for presentation at the given compute device; dynamicallybinding the selected supplemental content and the specified base videocontent to form a shareable media container instantiated by embed code,the shareable media container including a share user interface and atransaction user interface, the share user interface configured to allowa user to share an instance of the shareable media container to anothercompute device such that upon being shared to another compute device theembed code instantiates the specified base video content and theselected supplemental content, the transaction user interface configuredto allow a user to initiate and complete an e-commerce transactionassociated with at least one of the one or more products or serviceswithin the shareable media container without diverting from thespecified base content, the shareable media container being configuredto monitor and collect information related to the e-commercetransaction; and providing the shareable media container including thedynamically bound requested specified base video content, the selectedsupplemental content, and embed code to an endpoint associated with thegiven compute device for viewing by a user of the compute device. 22.The processor-implemented method of claim 21, further comprising:monitoring the plurality of compute device for one or more predefinedevents; responsive to a given compute device triggering one of thepredefined events, determining if the given compute device is associatedwith a crypto-currency wallet; and sending associated metadata to ablockchain if the given compute device is determined to be associatedwith a crypto-currency wallet.